Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate

Large variations in the growth of atmospheric methane, a prominent greenhouse gas, are driven by a diverse range of anthropogenic and natural emissions and by loss from oxidation by the hydroxyl radical. We used a decade-long dataset (2010–2019) of satellite observations of methane to show that tropical terrestrial emissions explain more than 80% of the observed changes in the global atmospheric methane growth rate over this period. Using correlative meteorological analyses, we show strong seasonal correlations (r = 0.6–0.8) between large-scale changes in sea surface temperature over the tropical oceans and regional variations in methane emissions (via changes in rainfall and temperature) over tropical South America and tropical Africa. Existing predictive skill for sea surface temperature variations could therefore be used to help forecast variations in global atmospheric methane.


Supplementary Text
A posteriori CH4 and CO2 Fluxes Supplementary Table 1 reports annual mean a priori and a posteriori net CO2 fluxes (PgC/yr) 5 corresponding to the CH4 emissions reported in Table 1  and 20 ppb but typically < 10ppb. Over tropical continents (30°S to 30°N), the mean biases are typically smaller. However, there are only five sites over this region, with only a small number of sites with contiguous measurement coverage of more than a few years.
Supplementary Figure 4 shows the Theil-Sen slopes (da Silva et al, 2015) of a posteriori CH4 fluxes, rainfall, skin temperature, and GRACE LWE from 2010 to 2019, inclusively. The main advantage of using the Theil-Sen estimator is to determine a long-term trend without being disproportionately influenced by short-term climate variations, e.g., 2015/2016 El Niño. The three main loci of positive CH4 emission trends are over tropical Africa and tropical South America. 5 Over eastern tropical Africa, we find the increase in a posteriori CH4 emissions (~0.4 g m -2 yr -1 per year) coincides with positive change in rainfall (up to 4 mm month -1 per year) and LWE (up to 0.5 cm per year) and mainly focused over the Sudd and Lake Victoria, consistent with recent work (Lunt et al, 2019). We also find a negative surface temperature trend, which is consistent with an increase in wetland extend (Lunt et al., 2019). To the west (15 o -25 o E, 6 o S-8 o N) we also 10 find a significant upward trend in emissions, driven by elevated rainfall during February-April in years after the recent El Niño. We find this increase is not significantly correlated with SST anomalies.
Over tropical South America, we find contrasting behaviour over the northeast and southwest of the continent. Over the northeast (mainly Colombia, Venezuela, Northeast Brazil and Guyana) we 15 find a reduction in a posteriori CH4 emissions corresponding to large drying trends that are reflected in the rainfall data and the GRACE LWE data. Similar drying trends have been reported for shorter periods by other studies (Marengo et al., 2016). In contrast, over southwest tropical South America (mainly Peru, Southwest Brazil and Bolivia), we find a significant increase in a posteriori CH4 emissions that corresponds to a significant increase in rainfall and LWE. 20 Supplementary Figure 5 shows the spatial distribution of Pearson correlation coefficients between the seasonal CMAP rainfall data and three metrics we use to describe SST-based metrics over the Pacific, Atlantic, and Indian Oceans, including Niño3.4, Indian Ocean Dipole (IOD) and Pacific-Atlantic SST Dipole (PAD). The PAD is defined at the gradient between the tropical Atlantic (50°W-30°W, 5°N-20°N) and Pacific (120°W-90°W, 5°N-20°N) Oceans. We use the NOAA OI 25 SST data to determine values for the IOD and PAD. Rainfall over the northeast and southwest Amazon (as defined above) are oppositely correlated with the Niño3.4 SST anomaly with maximal values of -0.6 and +0.5, respectively; we find a similar correlation pattern using the PAD. The contrasting responses of rainfall over the Tropical South America is due to changes in atmospheric circulation, specifically the Walker circulation, that are driven by SST anomalies (Wang et al., 30 2006;Espinoza et al., 2014;Barichivich et al., 2018;Gloor et al., 2015). Rainfall over tropical East Africa is best described by the IOD, as expected (e.g., Finney et al., 2020;Wainwright et al., 2019).
We further investigate the relationship between local rainfall, temperature and CH4 fluxes over the southwest and northeast Amazon and tropical East Africa, complementing the data reported in Table 2. We find the CH4 flux is strongly correlated with rainfall over the three regions, with values 5 from 0.4 to 0.8 (Supplementary Table 3). Over the dry tropics (e.g., northeast Africa and Northeast Amazon), we find negative correlations between -0.3 and -0.5 with temperature. Over wet tropical regions, we find that that influence of temperature is positive but weaker. Supplementary Table 4 is the corresponding version of Table 2 for tropical South America but using Niño 3.4 SST index instead of the Pacific-Atlantic dipole (PAD) SST metric (defined in the main text). We generally 10 find that over our study regions, where CH4 emissions have changed the most over our 2010-2019 study period ( Supplementary Fig. 4), the PAD SST metric better describes variation in CH4 fluxes than the Niño 3.4 SST index.

Supplementary Tables
Supplementary Table 1. Annual global and tropical terrestrial (i.e., TransCom=3 Tropical South America, North Africa, and Tropical Asia) net a priori and a posteriori CO2 fluxes (PgC/yr) from 2010 to 2019, inclusively. Uncertainties denote the 1-s value.

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Year Global CO2 Table 3. Correlations between a posteriori CH4 emission (g/m 2 /yr) and CMAP rainfall (mm/month), and MERRA data products that describe soil moisture (unitless), and surface temperature (K) from 2010 to 2019, inclusively, over NE and SW tropical South America and tropical Africa. We have used a three-month smoothing window for the CH4 emission to reduce the noise and to account for different time lags between CH4 fluxes and environmental variables. 5 Variables n, r, and p denote the number of data points, Pearson correlation coefficient, and the two-tail p-value.   Figure 4. Theil-Sen slopes determined from 2010 to 2019 for a CH4 emissions (g/m 2 /yr yr -1 ), b CMAP rainfall (mm/month yr -1 ), c MERRA2 skin temperature (K yr -1 ), and d GRACE LWE (cm yr -1 , 2010-2017). We only show data that have a trend significantly different 5 from zero (p<0.05). Coloured boxes in panel a denote three geographical regions where we report more detailed analysis.